library(rstan)
library(survival)
library(tidyverse)
library(tidybayes)
Stan_exponential_survival_model <- "
data{
int <lower=1> N_uncensored;
int <lower=1> N_censored;
int <lower=0> numCovariates;
matrix[N_censored, numCovariates] X_censored;
matrix[N_uncensored, numCovariates] X_uncensored;
vector <lower=0>[N_censored] times_censored;
vector <lower=0>[N_uncensored] times_uncensored;
}
parameters{
vector[numCovariates] beta; //regression coefficients
real alpha; //intercept
}
model{
beta ~ normal(0,10); //prior on regression coefficients
alpha ~ normal(0,10); //prior on intercept
target += exponential_lpdf(times_uncensored | exp(alpha+X_uncensored * beta)); //log-likelihood part for uncensored times
target += exponential_lccdf(times_censored | exp(alpha+X_censored * beta)); //log-likelihood for censored times
}
generated quantities{
vector[N_uncensored] times_uncensored_sampled; //prediction of death
for(i in 1:N_uncensored) {
times_uncensored_sampled[i] = exponential_rng(exp(alpha+X_uncensored[i,]* beta));
}
}
"
# prepare the data
set.seed(42);
require (tidyverse);
data <- read_csv('../data.csv')
New names:
* `` -> ...1
Rows: 2066 Columns: 12
── Column specification ───────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): url, host_type, project
dbl (8): ...1, major_releases, duration, author_count, rev_count, status, multi_repo, commits_per_day
lgl (1): commit_freq_above_median
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
N <- nrow (data);
X <- as.matrix(pull(data, major_releases));
is_censored <- pull(data, status)==0;
times <- pull(data, duration);
msk_censored <- is_censored == 1;
N_censored <- sum(msk_censored);
Stan_data <- list (N_uncensored = N - N_censored,
N_censored = N_censored,
numCovariates = ncol(X),
X_censored = as.matrix(X[msk_censored,]),
X_uncensored = as.matrix(X[!msk_censored ,]),
times_censored = times[msk_censored],
times_uncensored = times[!msk_censored])
# Fit Stan model
require(rstan)
exp_surv_model_fit <- suppressMessages(stan(model_code = Stan_exponential_survival_model, data = Stan_data))
Warning in system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
error in running command
clang: error: no input files
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000364 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.64 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 1.04487 seconds (Warm-up)
Chain 1: 0.941805 seconds (Sampling)
Chain 1: 1.98667 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0.000168 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.68 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 2:
Chain 2: Elapsed Time: 0.994872 seconds (Warm-up)
Chain 2: 0.916381 seconds (Sampling)
Chain 2: 1.91125 seconds (Total)
Chain 2:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0.00017 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.7 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 3:
Chain 3: Elapsed Time: 1.01847 seconds (Warm-up)
Chain 3: 0.976654 seconds (Sampling)
Chain 3: 1.99513 seconds (Total)
Chain 3:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0.000176 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.76 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 4:
Chain 4: Elapsed Time: 1.03277 seconds (Warm-up)
Chain 4: 0.782076 seconds (Sampling)
Chain 4: 1.81484 seconds (Total)
Chain 4:
print(get_seed(exp_surv_model_fit))
[1] 1781592037
fit_summary <- summary(exp_surv_model_fit)
print(fit_summary$summary)
mean se_mean sd 2.5% 25% 50%
beta[1] -1.443823 7.741168e-03 0.37556309 -2.269815 -1.672497 -1.416993
alpha -4.970648 7.041629e-04 0.03949185 -5.047143 -4.997410 -4.970395
times_uncensored_sampled[1] 144.282825 2.270819e+00 143.99604902 3.533011 42.631682 99.013253
times_uncensored_sampled[2] 142.049451 2.220240e+00 143.98011185 3.597634 41.297460 97.909677
times_uncensored_sampled[3] 143.681743 2.383723e+00 146.90173593 3.526873 40.166865 100.249813
times_uncensored_sampled[4] 147.940028 2.396533e+00 147.47844884 3.492281 42.198498 103.913578
times_uncensored_sampled[5] 144.072585 2.268754e+00 142.20799642 3.332208 42.567151 101.855460
times_uncensored_sampled[6] 141.459204 2.194185e+00 137.25167113 3.942252 41.348813 100.434474
times_uncensored_sampled[7] 146.396309 2.412435e+00 145.28463176 3.203863 42.866143 102.307336
times_uncensored_sampled[8] 143.001219 2.362735e+00 143.74343483 3.455600 40.665804 98.635272
times_uncensored_sampled[9] 144.335856 2.336259e+00 144.63102811 4.088224 40.547548 101.096353
times_uncensored_sampled[10] 147.111391 2.286544e+00 142.35902506 3.709886 44.315421 105.105785
times_uncensored_sampled[11] 146.528762 2.363002e+00 148.16365871 3.725527 42.723460 100.205743
times_uncensored_sampled[12] 149.020031 2.422250e+00 150.56216963 3.737042 42.857007 98.798149
times_uncensored_sampled[13] 145.815055 2.383654e+00 146.41888316 3.677345 39.886758 99.083650
times_uncensored_sampled[14] 142.019563 2.337650e+00 142.15558681 4.151210 39.360568 98.069704
times_uncensored_sampled[15] 143.633097 2.288964e+00 142.58768532 3.598670 42.670476 101.354741
times_uncensored_sampled[16] 143.417157 2.299210e+00 143.89888142 3.481819 40.343629 97.948237
times_uncensored_sampled[17] 144.698390 2.346819e+00 143.66384196 3.647003 41.019738 100.720369
times_uncensored_sampled[18] 140.608868 2.199956e+00 138.92005397 3.562913 40.184634 97.612878
times_uncensored_sampled[19] 145.051090 2.243761e+00 144.43637949 3.717424 41.390752 101.134265
times_uncensored_sampled[20] 148.814756 2.355899e+00 151.99359589 3.953208 41.570997 99.035485
times_uncensored_sampled[21] 145.313685 2.284689e+00 145.70851375 3.829402 43.233746 100.003299
times_uncensored_sampled[22] 144.549037 2.336422e+00 146.36846367 3.965298 40.002182 99.853245
times_uncensored_sampled[23] 147.466604 2.464049e+00 147.67448089 4.102182 43.701342 102.171753
times_uncensored_sampled[24] 147.509289 2.377058e+00 145.29530336 3.492302 44.142380 104.052366
times_uncensored_sampled[25] 142.016985 2.251500e+00 141.98864983 3.443717 39.591148 99.511185
times_uncensored_sampled[26] 143.192410 2.405582e+00 146.69653396 3.317775 40.124828 97.275403
times_uncensored_sampled[27] 137.770359 2.244729e+00 136.94499608 3.353410 39.308582 96.910231
times_uncensored_sampled[28] 143.282016 2.346454e+00 145.38638336 3.235814 41.368855 98.529101
times_uncensored_sampled[29] 143.871788 2.207890e+00 141.47101074 3.958167 42.941207 100.618405
times_uncensored_sampled[30] 145.088572 2.306396e+00 144.16620829 3.645319 42.610046 100.907433
times_uncensored_sampled[31] 142.719657 2.278574e+00 141.94586922 3.546179 41.476992 98.184856
times_uncensored_sampled[32] 140.051218 2.295105e+00 142.74993721 3.105984 39.326593 97.556878
times_uncensored_sampled[33] 142.178784 2.241846e+00 141.88055847 3.524278 40.505072 98.679621
times_uncensored_sampled[34] 145.801554 2.563088e+00 149.42124690 3.900367 40.689796 98.392061
times_uncensored_sampled[35] 145.879516 2.461091e+00 151.98857263 4.153748 41.403585 96.513313
times_uncensored_sampled[36] 143.234231 2.189528e+00 141.52480259 3.820332 41.667923 100.437659
times_uncensored_sampled[37] 139.812675 2.185740e+00 138.18667422 3.800950 41.075163 99.072104
times_uncensored_sampled[38] 143.519709 2.251572e+00 142.31646615 3.370824 41.964358 99.626226
times_uncensored_sampled[39] 142.793868 2.209649e+00 142.50019214 3.550388 41.012410 99.562957
times_uncensored_sampled[40] 141.461805 2.093582e+00 137.38391008 3.219756 41.864017 99.293080
times_uncensored_sampled[41] 144.460123 2.358926e+00 146.51556372 3.070663 42.282216 100.182875
times_uncensored_sampled[42] 145.339032 2.384344e+00 148.09500993 4.057736 41.668664 99.859916
times_uncensored_sampled[43] 144.334921 2.271561e+00 144.51700401 3.834080 41.881430 98.090594
times_uncensored_sampled[44] 146.912315 2.316457e+00 146.12111889 3.642973 41.976632 103.653147
times_uncensored_sampled[45] 141.234360 2.344186e+00 149.64304347 3.549195 38.941196 92.620201
times_uncensored_sampled[46] 143.568146 2.279709e+00 142.20697405 3.361309 40.571079 100.146449
times_uncensored_sampled[47] 144.007247 2.375325e+00 147.81373606 3.607610 39.311587 97.790493
times_uncensored_sampled[48] 141.510123 2.201146e+00 139.40529419 3.474515 41.202829 99.791470
times_uncensored_sampled[49] 146.138800 2.283095e+00 143.84853969 4.291024 42.880568 103.320078
times_uncensored_sampled[50] 140.079121 2.272051e+00 140.25151437 3.842098 41.188755 98.090586
times_uncensored_sampled[51] 147.864411 2.265270e+00 145.31773698 3.737743 44.825054 105.665093
times_uncensored_sampled[52] 146.769312 2.283992e+00 148.76796121 3.490648 41.283047 101.274938
times_uncensored_sampled[53] 140.558807 2.214036e+00 138.14543168 3.479361 41.490388 96.802662
times_uncensored_sampled[54] 144.362216 2.310476e+00 144.70660063 3.072260 41.330708 101.929344
times_uncensored_sampled[55] 144.815780 2.213933e+00 142.00418059 3.400743 41.817152 101.986213
times_uncensored_sampled[56] 147.224520 2.369569e+00 146.92160235 4.190908 43.059524 100.010094
times_uncensored_sampled[57] 148.516835 2.323984e+00 146.25981884 3.731607 43.007980 103.425354
times_uncensored_sampled[58] 147.466188 2.451235e+00 147.87267394 3.530621 43.704700 102.408963
times_uncensored_sampled[59] 146.247150 2.313343e+00 147.94423654 3.197958 40.800675 101.702331
times_uncensored_sampled[60] 142.646919 2.297262e+00 145.38660204 3.261655 39.406142 95.822694
times_uncensored_sampled[61] 146.779142 2.296909e+00 146.27532389 3.933223 41.826343 103.336411
times_uncensored_sampled[62] 143.865616 2.294611e+00 147.48811713 3.283372 41.817799 98.563118
times_uncensored_sampled[63] 141.087128 2.172258e+00 139.59805299 3.836860 38.759920 97.339037
times_uncensored_sampled[64] 144.561457 2.192162e+00 140.86793225 3.674060 42.193995 101.191939
times_uncensored_sampled[65] 144.635486 2.289666e+00 144.99386442 4.172843 42.425001 100.578781
times_uncensored_sampled[66] 142.415027 2.437683e+00 142.96499775 3.872056 40.157561 99.144183
times_uncensored_sampled[67] 144.412029 2.364486e+00 143.24145100 3.681522 41.937484 101.514677
times_uncensored_sampled[68] 144.967681 2.267685e+00 144.54250402 4.094467 42.628584 101.112635
times_uncensored_sampled[69] 145.616214 2.519803e+00 144.98110354 3.215748 40.478936 101.107535
times_uncensored_sampled[70] 146.645674 2.388842e+00 149.60568024 3.801788 41.042622 102.145912
times_uncensored_sampled[71] 144.190706 2.294334e+00 143.78476233 3.973352 39.145691 101.707035
times_uncensored_sampled[72] 142.191461 2.305302e+00 145.56731944 3.390068 40.842779 99.812088
times_uncensored_sampled[73] 146.830930 2.385948e+00 147.78177159 3.634404 42.174273 102.950814
times_uncensored_sampled[74] 144.711202 2.296045e+00 144.48118225 3.464418 41.661525 100.112510
times_uncensored_sampled[75] 141.488622 2.275692e+00 142.74792714 3.071740 38.889373 97.288471
times_uncensored_sampled[76] 143.737942 2.178045e+00 141.85460915 3.661001 40.267977 99.449284
times_uncensored_sampled[77] 141.660350 2.234000e+00 142.16055973 3.683461 41.261742 98.259845
times_uncensored_sampled[78] 145.273100 2.285990e+00 145.01152850 3.627593 39.426782 101.818732
times_uncensored_sampled[79] 144.631878 2.283563e+00 142.39709377 4.124197 42.561125 100.398470
times_uncensored_sampled[80] 143.185064 2.274880e+00 144.44978717 3.573561 40.957688 95.756127
times_uncensored_sampled[81] 145.929450 2.336856e+00 144.45918192 3.697612 43.992742 101.233657
times_uncensored_sampled[82] 140.796271 2.265435e+00 142.72116239 3.419260 39.523381 96.752140
times_uncensored_sampled[83] 143.292165 2.331730e+00 142.15072246 3.798934 41.726672 100.615830
times_uncensored_sampled[84] 150.168783 2.405436e+00 151.46796863 3.610563 43.065026 104.435474
times_uncensored_sampled[85] 143.566344 2.263366e+00 141.23126396 3.862335 43.515781 99.941240
times_uncensored_sampled[86] 144.978007 2.335859e+00 144.32398450 3.408278 41.127800 102.151164
times_uncensored_sampled[87] 146.398257 2.500830e+00 148.93100064 3.960118 41.845157 102.567995
times_uncensored_sampled[88] 147.143777 2.322813e+00 144.75664380 3.505223 43.281681 102.794827
times_uncensored_sampled[89] 144.509862 2.236111e+00 144.58513816 4.019491 42.626250 102.044526
times_uncensored_sampled[90] 142.455910 2.251183e+00 143.07305973 3.417098 39.420329 97.959825
times_uncensored_sampled[91] 145.289441 2.308068e+00 146.91408872 3.736474 42.365050 101.142873
times_uncensored_sampled[92] 142.695959 2.280783e+00 146.87379569 3.277844 41.179659 97.240406
times_uncensored_sampled[93] 145.994066 2.261820e+00 144.92316249 3.521301 44.376710 101.634127
times_uncensored_sampled[94] 143.338370 2.303467e+00 141.45327527 3.828928 41.261654 97.772096
times_uncensored_sampled[95] 145.322512 2.309336e+00 144.81735272 3.985905 42.595606 100.219408
times_uncensored_sampled[96] 143.196775 2.352259e+00 146.88606791 3.872517 40.776259 95.406520
times_uncensored_sampled[97] 142.195894 2.190794e+00 140.17784445 3.617841 41.471628 99.615128
times_uncensored_sampled[98] 143.412946 2.333277e+00 145.02137030 3.655814 38.717505 98.536042
75% 97.5% n_eff Rhat
beta[1] -1.184759 -0.7710732 2353.709 1.0005282
alpha -4.942197 -4.8952610 3145.349 0.9999338
times_uncensored_sampled[1] 203.273879 529.3350838 4021.019 1.0006641
times_uncensored_sampled[2] 192.397401 543.2946941 4205.380 0.9996345
times_uncensored_sampled[3] 196.431606 537.7358525 3797.888 0.9995316
times_uncensored_sampled[4] 206.750579 552.5698700 3786.956 0.9996655
times_uncensored_sampled[5] 198.249420 526.9757562 3928.920 1.0009630
times_uncensored_sampled[6] 197.987098 521.7031163 3912.809 0.9997256
times_uncensored_sampled[7] 201.750808 540.6731195 3626.838 1.0008689
times_uncensored_sampled[8] 200.704310 523.1983841 3701.229 0.9994011
times_uncensored_sampled[9] 202.000052 527.3956789 3832.489 0.9995421
times_uncensored_sampled[10] 203.571507 515.2868368 3876.243 0.9994476
times_uncensored_sampled[11] 200.928320 550.9088984 3931.473 0.9991638
times_uncensored_sampled[12] 206.457949 549.6471889 3863.614 1.0010614
times_uncensored_sampled[13] 204.013470 541.8197924 3773.183 1.0014249
times_uncensored_sampled[14] 194.369170 517.9371979 3698.018 1.0007314
times_uncensored_sampled[15] 200.408142 521.9892510 3880.487 1.0002020
times_uncensored_sampled[16] 197.727118 528.0525905 3917.036 0.9996031
times_uncensored_sampled[17] 202.088063 534.5643330 3747.449 1.0009426
times_uncensored_sampled[18] 193.968199 534.1569944 3987.511 0.9997016
times_uncensored_sampled[19] 200.200959 541.0848538 4143.813 0.9995590
times_uncensored_sampled[20] 206.525047 558.2741244 4162.338 0.9999818
times_uncensored_sampled[21] 198.377063 553.6842429 4067.388 0.9998197
times_uncensored_sampled[22] 198.863991 523.2290036 3924.575 0.9995257
times_uncensored_sampled[23] 204.413866 549.0375743 3591.800 0.9997577
times_uncensored_sampled[24] 202.981878 546.3531349 3736.145 1.0003559
times_uncensored_sampled[25] 200.057031 514.6716842 3977.073 1.0003631
times_uncensored_sampled[26] 196.504778 536.8116119 3718.770 1.0003746
times_uncensored_sampled[27] 190.970343 519.4883820 3721.897 0.9996333
times_uncensored_sampled[28] 195.206522 546.6138806 3839.046 1.0006058
times_uncensored_sampled[29] 198.960349 523.6339778 4105.634 0.9997565
times_uncensored_sampled[30] 202.335776 540.1239303 3907.141 1.0005862
times_uncensored_sampled[31] 195.785339 506.8563190 3880.784 0.9990611
times_uncensored_sampled[32] 193.518278 523.8499276 3868.536 0.9994216
times_uncensored_sampled[33] 195.564715 525.5384020 4005.292 1.0010819
times_uncensored_sampled[34] 201.770879 544.6281098 3398.581 1.0005868
times_uncensored_sampled[35] 198.352793 552.8106796 3813.875 0.9998591
times_uncensored_sampled[36] 196.991616 530.6394844 4177.957 0.9993079
times_uncensored_sampled[37] 192.642138 506.5656760 3997.010 0.9999563
times_uncensored_sampled[38] 198.298359 516.5395471 3995.201 0.9995522
times_uncensored_sampled[39] 198.406125 533.7245553 4158.956 0.9999016
times_uncensored_sampled[40] 199.010637 516.9831836 4306.178 0.9995443
times_uncensored_sampled[41] 199.476944 533.9606553 3857.794 1.0005261
times_uncensored_sampled[42] 197.044421 550.0275779 3857.829 0.9999901
times_uncensored_sampled[43] 199.463220 530.0697749 4047.522 1.0004064
times_uncensored_sampled[44] 209.308219 536.8236734 3979.031 0.9997137
times_uncensored_sampled[45] 190.181393 547.9942485 4075.011 0.9993920
times_uncensored_sampled[46] 202.180964 512.9227377 3891.195 0.9995927
times_uncensored_sampled[47] 199.544368 533.1165892 3872.430 0.9999772
times_uncensored_sampled[48] 197.674842 504.9013273 4011.076 1.0004068
times_uncensored_sampled[49] 204.014614 518.0433868 3969.748 1.0002660
times_uncensored_sampled[50] 192.344902 527.0716821 3810.475 1.0004348
times_uncensored_sampled[51] 203.015844 551.6811206 4115.261 0.9997718
times_uncensored_sampled[52] 200.365212 542.0418143 4242.577 1.0002386
times_uncensored_sampled[53] 194.785500 521.4662889 3893.173 0.9995966
times_uncensored_sampled[54] 201.363129 518.7394479 3922.598 0.9993780
times_uncensored_sampled[55] 203.606903 517.2742484 4114.087 0.9992433
times_uncensored_sampled[56] 203.092805 548.3881803 3844.434 0.9992525
times_uncensored_sampled[57] 208.681660 537.2593257 3960.809 0.9998864
times_uncensored_sampled[58] 202.774602 561.4514679 3639.199 1.0004360
times_uncensored_sampled[59] 199.461391 548.2524395 4089.931 1.0004229
times_uncensored_sampled[60] 197.347913 532.7997217 4005.232 0.9995835
times_uncensored_sampled[61] 202.227467 543.1142693 4055.594 0.9998767
times_uncensored_sampled[62] 197.135531 530.0838017 4131.387 0.9999286
times_uncensored_sampled[63] 197.520810 511.1941401 4129.864 0.9994763
times_uncensored_sampled[64] 201.868626 524.2925318 4129.324 0.9994295
times_uncensored_sampled[65] 196.064663 529.4288402 4010.100 1.0005010
times_uncensored_sampled[66] 196.847267 520.6327709 3439.576 0.9995612
times_uncensored_sampled[67] 197.754368 530.2855270 3669.983 1.0004197
times_uncensored_sampled[68] 203.030846 534.0481583 4062.804 0.9995391
times_uncensored_sampled[69] 204.626780 534.8426058 3310.469 1.0005905
times_uncensored_sampled[70] 199.073409 556.0940983 3922.124 0.9999916
times_uncensored_sampled[71] 203.898841 524.6755715 3927.466 1.0012705
times_uncensored_sampled[72] 196.092198 519.6076121 3987.237 1.0001658
times_uncensored_sampled[73] 204.303270 542.3965371 3836.362 0.9999543
times_uncensored_sampled[74] 201.591826 541.0279393 3959.697 1.0000814
times_uncensored_sampled[75] 195.722819 524.7481510 3934.709 1.0001302
times_uncensored_sampled[76] 201.079497 509.0865133 4241.829 0.9997263
times_uncensored_sampled[77] 193.980598 531.0454820 4049.411 0.9997303
times_uncensored_sampled[78] 204.266895 531.5536853 4023.986 1.0007362
times_uncensored_sampled[79] 203.086613 528.0050740 3888.447 0.9998760
times_uncensored_sampled[80] 198.710197 544.2139274 4031.964 0.9996293
times_uncensored_sampled[81] 203.379213 537.7619275 3821.435 0.9993326
times_uncensored_sampled[82] 195.753268 529.2473350 3968.931 1.0000777
times_uncensored_sampled[83] 196.717166 535.5867097 3716.564 1.0004427
times_uncensored_sampled[84] 209.715471 555.9776134 3965.099 1.0001464
times_uncensored_sampled[85] 200.005397 507.2338442 3893.606 1.0006397
times_uncensored_sampled[86] 201.451121 536.3697813 3817.541 0.9998703
times_uncensored_sampled[87] 197.126275 554.9081798 3546.515 1.0015126
times_uncensored_sampled[88] 203.790693 549.4351180 3883.727 0.9998204
times_uncensored_sampled[89] 198.953402 536.9199643 4180.812 1.0005915
times_uncensored_sampled[90] 198.796912 513.2883391 4039.188 1.0000612
times_uncensored_sampled[91] 201.972885 535.4546581 4051.628 1.0001434
times_uncensored_sampled[92] 195.570060 539.2370172 4146.871 0.9998138
times_uncensored_sampled[93] 203.702991 529.9039039 4105.437 0.9998317
times_uncensored_sampled[94] 201.636581 525.7576150 3771.046 1.0000440
times_uncensored_sampled[95] 202.099370 522.7787019 3932.484 1.0001446
times_uncensored_sampled[96] 196.872349 539.6454847 3899.340 0.9996693
times_uncensored_sampled[97] 193.406199 533.3815402 4094.074 1.0001751
times_uncensored_sampled[98] 195.913475 539.4857586 3863.058 1.0004989
[ reached getOption("max.print") -- omitted 547 rows ]
exp_surv_model_draws <- tidybayes::tidy_draws(exp_surv_model_fit)
exp_surv_model_draws